Sub-mesoscale temperature variability in observed and simulated convective cold pools

The spatial and temporal variability of air temperature represents the imprint of various meteorological processes, ranging from microscale turbulence to synoptic-scale weather systems. Convective cold pools, formed by evaporatively cooled downdrafts of precipitating clouds, are known to be an important source of mesoscale variability over mid-latitude land. Cold pools both directly perturb the near-surface temperature field and influence variability by controlling larger-scale convective organization. However, their impact on the sub-mesoscale (100 m to 10 km) temperature variability is unclear due to insufficient observational data. Consequently, the validation of sub-mesoscale variability in numerical weather prediction (NWP) and Large-Eddy Simulation (LES) models is also impeded.

In this study, we apply the variogram framework to determine sub-mesoscale temperature variability in observations as well as in idealized and realistic simulations of cold pool events. The basis of the analyses are actual and virtual observations of a dense network of 99 surface measurement stations as part of the Field Experiment on Submesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) conducted in eastern Germany during summer 2021. The observed variogram averaged over the lifetime of a cold pool shows enhanced temperature variance at scales between about 1 km and 15 km compared to well-mixed boundary layer conditions, although the magnitude of the perturbation strongly varies for single time steps. Except for the intensification phase, the cold pool generally reduces the temperature variability at sub-km scales compared to pre-cold pool conditions. This suggests smoothing of sub-km temperature gradients by enhanced mixing near the surface as well as damped turbulent surface fluxes.

Idealized cold pool simulations at LES grid spacings capture the overall variogram shape and evolution well but show the largest uncertainty for sub-km scales as compared to the observed variograms. The results are sensitive to the sampled lifetime stage of the cold pool, its environmental conditions, and the model representation of dissipation time scales and turbulent surface fluxes. These findings can help to identify the spatial and temporal scales of variability that are relevant to correctly simulate convective processes in the atmosphere and their interaction with the land surface.